In-class Exercise 4: 2nd Order Spatial Point Patterns Analysis Methods

Published

January 30, 2023

Modified

February 8, 2023

1 Import R Packages

pacman::p_load(sf, tidyverse, funModeling, tmap)

2 Import Datasets

2.1 WPdx+ Dataset

wp_osun <- read_csv("data/aspatial/Water_Point_Data_Exchange_-_Plus__WPdx__.csv") %>% filter(`#clean_country_name` == "Nigeria" &
                                                                                              `#clean_adm1` == "Osun")

2.2 Nigeria Osun State

NGA <- st_read(dsn = "data/geospatial/nga_adm_osgof_20190417",
               layer = "nga_admbnda_adm2_osgof_20190417") %>%
  st_transform(crs = 26392)

Reading layer `nga_admbnda_adm2_osgof_20190417' from data source 
  `C:\deadline2359\IS415-GAA\Take-home_Ex\Take-home_Ex01\data\geospatial\nga_adm_osgof_20190417' 
  using driver `ESRI Shapefile'
Simple feature collection with 774 features and 16 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 2.668534 ymin: 4.273007 xmax: 14.67882 ymax: 13.89442
Geodetic CRS:  WGS 84

3 Data Handling

3.1 WPdx+ Dataset

wp_osun$Geometry = st_as_sfc(wp_osun$`New Georeferenced Column`)
wp_osun

# A tibble: 5,557 × 71
   row_id `#source`      #lat_…¹ #lon_…² #repo…³ #stat…⁴ #wate…⁵ #wate…⁶ #wate…⁷
    <dbl> <chr>            <dbl>   <dbl> <chr>   <chr>   <chr>   <chr>   <chr>  
 1 429123 GRID3             8.02    5.06 08/29/… Unknown <NA>    <NA>    Tapsta…
 2  70566 Federal Minis…    7.32    4.79 05/11/… No      Protec… Well    Mechan…
 3  70578 Federal Minis…    7.76    4.56 05/11/… No      Boreho… Well    Mechan…
 4  66401 Federal Minis…    8.03    4.64 04/30/… No      Boreho… Well    Mechan…
 5 422190 GRID3             7.87    4.88 08/29/… Unknown <NA>    <NA>    Tapsta…
 6 422064 GRID3             7.7     4.89 08/29/… Unknown <NA>    <NA>    Tapsta…
 7  65607 Federal Minis…    7.89    4.71 05/12/… No      Boreho… Well    Mechan…
 8  68989 Federal Minis…    7.51    4.27 05/07/… No      Boreho… Well    <NA>   
 9  67708 Federal Minis…    7.48    4.35 04/29/… Yes     Boreho… Well    Mechan…
10  66419 Federal Minis…    7.63    4.50 05/08/… Yes     Boreho… Well    Hand P…
# … with 5,547 more rows, 62 more variables: `#water_tech_category` <chr>,
#   `#facility_type` <chr>, `#clean_country_name` <chr>, `#clean_adm1` <chr>,
#   `#clean_adm2` <chr>, `#clean_adm3` <chr>, `#clean_adm4` <chr>,
#   `#install_year` <dbl>, `#installer` <chr>, `#rehab_year` <lgl>,
#   `#rehabilitator` <lgl>, `#management_clean` <chr>, `#status_clean` <chr>,
#   `#pay` <chr>, `#fecal_coliform_presence` <chr>,
#   `#fecal_coliform_value` <dbl>, `#subjective_quality` <chr>, …

3.1.0.1 Create Simple Feature DataFrame

wp_sf <- st_sf(wp_osun, crs=4326)

wp_sf <- wp_sf %>%
  st_transform(crs = 26392)

4 Geospatial Data Cleaning

4.1 Excluding Redundant Fields

NGA <- NGA  %>%
  filter(`ADM1_EN` == "Osun") %>%
  select(c(3:4, 8:9))
NGA

Simple feature collection with 30 features and 4 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 176503.2 ymin: 331434.7 xmax: 291043.8 ymax: 454520.1
Projected CRS: Minna / Nigeria Mid Belt
First 10 features:
          ADM2_EN ADM2_PCODE ADM1_EN ADM1_PCODE                       geometry
1        Aiyedade   NG030001    Osun      NG030 MULTIPOLYGON (((213526.6 34...
2        Aiyedire   NG030002    Osun      NG030 MULTIPOLYGON (((212542.6 40...
3  Atakumosa East   NG030003    Osun      NG030 MULTIPOLYGON (((265746.8 37...
4  Atakumosa West   NG030004    Osun      NG030 MULTIPOLYGON (((248871.4 40...
5      Boluwaduro   NG030005    Osun      NG030 MULTIPOLYGON (((266092.2 43...
6          Boripe   NG030006    Osun      NG030 MULTIPOLYGON (((255072.5 43...
7       Ede North   NG030007    Osun      NG030 MULTIPOLYGON (((236386.9 41...
8       Ede South   NG030008    Osun      NG030 MULTIPOLYGON (((236386.9 41...
9        Egbedore   NG030009    Osun      NG030 MULTIPOLYGON (((220756 4317...
10         Ejigbo   NG030010    Osun      NG030 MULTIPOLYGON (((214422.1 42...

4.2 Checking for Duplicated Name

You can see that no duplicated LGAs in the Osun state.

NGA$ADM2_EN[duplicated(NGA$ADM2_EN) == TRUE]

character(0)

wp_sf <- st_intersection(NGA, wp_sf)

5 Data Wrangling for Water Point Data

freq(data = wp_sf,
     input = 'X.status_clean')

                    X.status_clean frequency percentage cumulative_perc
1                       Functional      2232      41.94           41.94
2                   Non-Functional      1894      35.59           77.53
3                             <NA>       734      13.79           91.32
4      Functional but needs repair       236       4.43           95.75
5 Non-Functional due to dry season       146       2.74           98.49
6        Functional but not in use        61       1.15           99.64
7                        Abandoned        15       0.28           99.92
8         Abandoned/Decommissioned         4       0.08          100.00

wp_sf_nga <- wp_sf %>%
  rename(status_clean = 'X.status_clean') %>%
  select(status_clean) %>%
  mutate(status_clean = replace_na(
    status_clean, "unknown" # rename NA to be called "unknown"
  ))

freq(data = wp_sf_nga,
     input = 'status_clean')

                      status_clean frequency percentage cumulative_perc
1                       Functional      2232      41.94           41.94
2                   Non-Functional      1894      35.59           77.53
3                          unknown       734      13.79           91.32
4      Functional but needs repair       236       4.43           95.75
5 Non-Functional due to dry season       146       2.74           98.49
6        Functional but not in use        61       1.15           99.64
7                        Abandoned        15       0.28           99.92
8         Abandoned/Decommissioned         4       0.08          100.00

5.1 Extract Water Point Data

wp_functional <- wp_sf_nga %>%
  filter(status_clean %in%
           c("Functional",
             "Functional but not in use",
             "Functional but needs repair"))

wp_nonfunctional <- wp_sf_nga %>%
  filter(status_clean %in%
           c("Abandoned/Decommissioned",
             "Abandoned",
             "Non-Functional due to dry season",
             "Non-Functional",
             "Non functional due to dry season"))

wp_unknown <- wp_sf_nga %>%
  filter(status_clean == "unknown")

5.2 Performing Point-in-Polygon Count

NGA_wp <- NGA %>%
  mutate('total_wp' = lengths(
    st_intersects(NGA, wp_sf_nga))) %>%
  mutate('wp_functional' = lengths(
    st_intersects(NGA, wp_functional))) %>%
  mutate('wp_nonfunctional' = lengths(
    st_intersects(NGA, wp_nonfunctional))) %>%
  mutate('wp_unknown' = lengths(
    st_intersects(NGA, wp_unknown)))
NGA_wp

Simple feature collection with 30 features and 8 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 176503.2 ymin: 331434.7 xmax: 291043.8 ymax: 454520.1
Projected CRS: Minna / Nigeria Mid Belt
First 10 features:
          ADM2_EN ADM2_PCODE ADM1_EN ADM1_PCODE                       geometry
1        Aiyedade   NG030001    Osun      NG030 MULTIPOLYGON (((213526.6 34...
2        Aiyedire   NG030002    Osun      NG030 MULTIPOLYGON (((212542.6 40...
3  Atakumosa East   NG030003    Osun      NG030 MULTIPOLYGON (((265746.8 37...
4  Atakumosa West   NG030004    Osun      NG030 MULTIPOLYGON (((248871.4 40...
5      Boluwaduro   NG030005    Osun      NG030 MULTIPOLYGON (((266092.2 43...
6          Boripe   NG030006    Osun      NG030 MULTIPOLYGON (((255072.5 43...
7       Ede North   NG030007    Osun      NG030 MULTIPOLYGON (((236386.9 41...
8       Ede South   NG030008    Osun      NG030 MULTIPOLYGON (((236386.9 41...
9        Egbedore   NG030009    Osun      NG030 MULTIPOLYGON (((220756 4317...
10         Ejigbo   NG030010    Osun      NG030 MULTIPOLYGON (((214422.1 42...
   total_wp wp_functional wp_nonfunctional wp_unknown
1       389           157              154         78
2       175            89               57         29
3       223            98               92         33
4       246           111              103         32
5       129            63               51         15
6       177            79               85         13
7       216           141               50         25
8       146            72               39         35
9       142            63               44         35
10      434           274              126         34

ggplot(data = NGA_wp,
       aes(x = total_wp)) + 
  geom_histogram(bins=20,
                 color="black",
                 fill="light blue") +
  geom_vline(aes(xintercept=mean(
    total_wp, na.rm=T)),
             color="red", 
             linetype="dashed", 
             size=0.8) +
  ggtitle("Distribution of total water points by LGA") +
  xlab("No. of water points") +
  ylab("No. of\nLGAs") +
  theme(axis.title.y=element_text(angle = 0))

6 First-order Spatial Point Patterns Analysis

Creating a simple interactive map, we can easily see that most of the water points are located towards the north of Osun, leaving the south with little.

tmap_mode("view")
tm_shape(wp_functional) +
 tm_dots(col = "status_clean",
         pal = "lightblue",
         title = "Functional") +
tm_shape(wp_nonfunctional) +
 tm_dots(col = "status_clean",
         pal = "orange",
         title = "Non-Functional") +
  tm_view(set.zoom.limits = c(5,25),
          set.view = 9)

Looking at the functional and non-functional water points separately, both maps reflect the same trend as the above map where the water points are largely located towards the north.

Functional
Non-Functional

tmap_mode("view")
tm_shape(wp_functional) +
 tm_dots(col = "status_clean",
         title = "Functional",
         palette = "Blues") +
  tm_view(set.zoom.limits = c(5,25),
          set.view = 9)

tmap_mode("view")
tm_shape(wp_nonfunctional) +
 tm_dots(col = "status_clean",
         title = "Non-Functional",
         palette = "Oranges") +
  tm_view(set.zoom.limits = c(5,25),
          set.view = 9)

tmap_mode("plot")

7 Exploratory Spatial Data Analysis

In-class Exercise 4: 2nd Order Spatial Point Patterns Analysis Methods

1 Import R Packages

2 Import Datasets

2.1 WPdx+ Dataset

2.2 Nigeria Osun State

3 Data Handling

3.1 WPdx+ Dataset

3.1.0.1 Create Simple Feature DataFrame

4 Geospatial Data Cleaning

4.1 Excluding Redundant Fields

4.2 Checking for Duplicated Name

5 Data Wrangling for Water Point Data

5.1 Extract Water Point Data

5.2 Performing Point-in-Polygon Count

6 First-order Spatial Point Patterns Analysis

7 Exploratory Spatial Data Analysis

7.1 Kernel Density Maps

7.1.1 Conversion of Datatypes

7.1.1.1 **Converting sf data frames to sp's Spatial* class**

1 Import R Packages

2 Import Datasets

2.1 WPdx+ Dataset

2.2 Nigeria Osun State

3 Data Handling

3.1 WPdx+ Dataset

3.1.0.1 Create Simple Feature DataFrame

4 Geospatial Data Cleaning

4.1 Excluding Redundant Fields

4.2 Checking for Duplicated Name

5 Data Wrangling for Water Point Data

5.1 Extract Water Point Data

5.2 Performing Point-in-Polygon Count

6 First-order Spatial Point Patterns Analysis

7 Exploratory Spatial Data Analysis

7.1 Kernel Density Maps

7.1.1 Conversion of Datatypes

7.1.1.1 Converting sf data frames to sp's Spatial* class

7.1.1.1 **Converting sf data frames to sp's Spatial* class**